partition

Module Contents

DataPartitioner	Base class for data partition in federated learning.
CIFAR10Partitioner	CIFAR10 data partitioner.
CIFAR100Partitioner	CIFAR100 data partitioner.
BasicPartitioner	label-distribution-skew:quantity-based
VisionPartitioner	label-distribution-skew:quantity-based
MNISTPartitioner	label-distribution-skew:quantity-based
FMNISTPartitioner	label-distribution-skew:quantity-based
SVHNPartitioner	label-distribution-skew:quantity-based
FCUBEPartitioner	FCUBE data partitioner.
AdultPartitioner	label-distribution-skew:quantity-based
RCV1Partitioner	label-distribution-skew:quantity-based
CovtypePartitioner	label-distribution-skew:quantity-based

class DataPartitioner

Bases: abc.ABC

Base class for data partition in federated learning.

abstract _perform_partition(self)

abstract __getitem__(self, index)

abstract __len__(self)

class CIFAR10Partitioner(targets, num_clients, balance=True, partition='iid', unbalance_sgm=0, num_shards=None, dir_alpha=None, verbose=True, seed=None)

Bases: DataPartitioner

CIFAR10 data partitioner.

Partition CIFAR10 given specific client number. Currently 6 supported partition schemes can be achieved by passing different combination of parameters in initialization:

• balance=None

  • partition="dirichlet": non-iid partition used in Bayesian Nonparametric Federated Learning of Neural Networks and Federated Learning with Matched Averaging. Refer to fedlab.utils.dataset.functional.hetero_dir_partition() for more information.

  • partition="shards": non-iid method used in FedAvg paper. Refer to fedlab.utils.dataset.functional.shards_partition() for more information.

• balance=True: “Balance” refers to FL scenario that sample numbers for different clients are the same. Refer to fedlab.utils.dataset.functional.balance_partition() for more information.

  • partition="iid": Random select samples from complete dataset given sample number for each client.

  • partition="dirichlet": Refer to fedlab.utils.dataset.functional.client_inner_dirichlet_partition() for more information.

• balance=False: “Unbalance” refers to FL scenario that sample numbers for different clients are different. For unbalance method, sample number for each client is drown from Log-Normal distribution with variance unbalanced_sgm. When unbalanced_sgm=0, partition is balanced. Refer to fedlab.utils.dataset.functional.lognormal_unbalance_partition() for more information. The method is from paper Federated Learning Based on Dynamic Regularization.

  • partition="iid": Random select samples from complete dataset given sample number for each client.

  • partition="dirichlet": Refer to fedlab.utils.dataset.functional.client_inner_dirichlet_partition() for more information.

Parameters

• targets (list or numpy.ndarray) – Targets of dataset for partition. Each element is in range of [0, 1, …, 9].

• num_clients (int) – Number of clients for data partition.

• balance (bool, optional) – Balanced partition over all clients or not. Default as True.

• partition (str, optional) – Partition type, only "iid", shards, "dirichlet" are supported. Default as "iid".

• unbalance_sgm (float, optional) – Log-normal distribution variance for unbalanced data partition over clients. Default as 0 for balanced partition.

• num_shards (int, optional) – Number of shards in non-iid "shards" partition. Only works if partition="shards". Default as None.

• dir_alpha (float, optional) – Dirichlet distribution parameter for non-iid partition. Only works if partition="dirichlet". Default as None.

• verbose (bool, optional) – Whether to print partition process. Default as True.

• seed (int, optional) – Random seed. Default as None.

num_classes = 10

_perform_partition(self)

__getitem__(self, index)

Obtain sample indices for client index.

Parameters

index (int) – Client ID.

Returns

List of sample indices for client ID index.

Return type

list

__len__(self)

Usually equals to number of clients.

class CIFAR100Partitioner(targets, num_clients, balance=True, partition='iid', unbalance_sgm=0, num_shards=None, dir_alpha=None, verbose=True, seed=None)

Bases: CIFAR10Partitioner

CIFAR100 data partitioner.

This is a subclass of the CIFAR10Partitioner.

num_classes = 100

class BasicPartitioner(targets, num_clients, partition='iid', dir_alpha=None, major_classes_num=1, verbose=True, seed=None)

Bases: DataPartitioner

• label-distribution-skew:quantity-based

• label-distribution-skew:distributed-based (Dirichlet)

• quantity-skew (Dirichlet)

• IID

Parameters

• targets –

• num_clients –

• partition –

• dir_alpha –

• major_classes_num –

• verbose –

• seed –

num_classes = 2

_perform_partition(self)

__getitem__(self, index)

__len__(self)

class VisionPartitioner(targets, num_clients, partition='iid', dir_alpha=None, major_classes_num=None, verbose=True, seed=None)

Bases: BasicPartitioner

• label-distribution-skew:quantity-based

• label-distribution-skew:distributed-based (Dirichlet)

• quantity-skew (Dirichlet)

• IID

Parameters

• targets –

• num_clients –

• partition –

• dir_alpha –

• major_classes_num –

• verbose –

• seed –

num_classes = 10

class MNISTPartitioner(targets, num_clients, partition='iid', dir_alpha=None, major_classes_num=None, verbose=True, seed=None)

Bases: VisionPartitioner

• label-distribution-skew:quantity-based

• label-distribution-skew:distributed-based (Dirichlet)

• quantity-skew (Dirichlet)

• IID

Parameters

• targets –

• num_clients –

• partition –

• dir_alpha –

• major_classes_num –

• verbose –

• seed –

num_features = 784

class FMNISTPartitioner(targets, num_clients, partition='iid', dir_alpha=None, major_classes_num=None, verbose=True, seed=None)

Bases: VisionPartitioner

• label-distribution-skew:quantity-based

• label-distribution-skew:distributed-based (Dirichlet)

• quantity-skew (Dirichlet)

• IID

Parameters

• targets –

• num_clients –

• partition –

• dir_alpha –

• major_classes_num –

• verbose –

• seed –

num_features = 784

class SVHNPartitioner(targets, num_clients, partition='iid', dir_alpha=None, major_classes_num=None, verbose=True, seed=None)

Bases: VisionPartitioner

• label-distribution-skew:quantity-based

• label-distribution-skew:distributed-based (Dirichlet)

• quantity-skew (Dirichlet)

• IID

Parameters

• targets –

• num_clients –

• partition –

• dir_alpha –

• major_classes_num –

• verbose –

• seed –

num_features = 1024

class FCUBEPartitioner(data, partition)

Bases: DataPartitioner

FCUBE data partitioner.

FCUBE is a synthetic dataset for research in non-IID scenario with feature imbalance. This dataset and its partition methods are proposed in Federated Learning on Non-IID Data Silos: An Experimental Study.

Supported partition methods for FCUBE:

• feature-distribution-skew:synthetic

• IID

For more details, please refer to Section (IV-B-b) of original paper.

Parameters

data (numpy.ndarray) – Data of dataset FCUBE.

num_classes = 2

num_clients = 4

_perform_partition(self)

__getitem__(self, index)

__len__(self)

class AdultPartitioner(targets, num_clients, partition='iid', dir_alpha=None, major_classes_num=1, verbose=True, seed=None)

Bases: BasicPartitioner

• label-distribution-skew:quantity-based

• label-distribution-skew:distributed-based (Dirichlet)

• quantity-skew (Dirichlet)

• IID

Parameters

• targets –

• num_clients –

• partition –

• dir_alpha –

• major_classes_num –

• verbose –

• seed –

num_features = 123

num_classes = 2

class RCV1Partitioner(targets, num_clients, partition='iid', dir_alpha=None, major_classes_num=1, verbose=True, seed=None)

Bases: BasicPartitioner

• label-distribution-skew:quantity-based

• label-distribution-skew:distributed-based (Dirichlet)

• quantity-skew (Dirichlet)

• IID

Parameters

• targets –

• num_clients –

• partition –

• dir_alpha –

• major_classes_num –

• verbose –

• seed –

num_features = 47236

num_classes = 2

class CovtypePartitioner(targets, num_clients, partition='iid', dir_alpha=None, major_classes_num=1, verbose=True, seed=None)

Bases: BasicPartitioner

• label-distribution-skew:quantity-based

• label-distribution-skew:distributed-based (Dirichlet)

• quantity-skew (Dirichlet)

• IID

Parameters

• targets –

• num_clients –

• partition –

• dir_alpha –

• major_classes_num –

• verbose –

• seed –

num_features = 54

num_classes = 2